The CEO of Datycs applies lessons from standardizing wireless networks in the 90s to today's healthcare challenges. He compares siloed EHRs to old proprietary cell towers, highlighting how open standards like FHIR can solve a problem that the telecom industry conquered decades ago.
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The majority of what payers identify as 'care gaps' are actually 'data gaps'—a lack of information leads to an assumption of missing care. By solving the data acquisition problem first, organizations can distinguish between the two. This dramatically shrinks the problem set, focusing expensive outreach efforts only on patients with true care needs.
Companies struggle with AI not because of the models, but because their data is siloed. Adopting an 'integration-first' mindset is crucial for creating the unified data foundation AI requires.
Datycs proactively converted unstructured data into FHIR resources long before clients were ready to use them. This future-proofed their platform, positioning them ahead of the curve when interoperability regulations finally mandated such standards, eliminating the need for custom APIs.
The next evolution in personalized medicine will be interoperability between personal and clinical AIs. A patient's AI, rich with daily context, will interface with their doctor's AI, trained on clinical data, to create a shared understanding before the human consultation begins.
Despite the hype, Datycs' CEO finds that even fine-tuned healthcare LLMs struggle with the real-world complexity and messiness of clinical notes. This reality check highlights the ongoing need for specialized NLP and domain-specific tools to achieve accuracy in healthcare.
Before deploying AI across a business, companies must first harmonize data definitions, especially after mergers. When different units call a "raw lead" something different, AI models cannot function reliably. This foundational data work is a critical prerequisite for moving beyond proofs-of-concept to scalable AI solutions.
Chronic disease patients face a cascade of interconnected problems: pre-authorizations, pharmacy stockouts, and incomprehensible insurance rules. AI's potential lies in acting as an intelligent agent to navigate this complex, fragmented system on behalf of the patient, reducing waste and improving outcomes.
Contrary to advisors who predicted EHRs would quickly fix healthcare data, Matt Holt invested in HealthPort, believing the system's deep brokenness provided a 15-20 year runway. The insight is that intractable, systemic fragmentation creates durable opportunities for foundational infrastructure players.
CEO Srini Rawl explains that while many companies focused on structured healthcare data, Datycs targeted complex, unstructured documents. This challenging niche became their competitive advantage, creating a significant data and experience moat after processing over 15 million clinical charts.
According to Salesforce's AI chief, the primary challenge for large companies deploying AI is harmonizing data across siloed departments, like sales and marketing. AI cannot operate effectively without connected, unified data, making data integration the crucial first step before any advanced AI implementation.
